Courses

These course descriptions provide a general overview of course coverage and credit awarded (in parentheses following the course name). Our faculty routinely update and adapt offerings to address new trends in the subject area and based on student feedback. For information on degree requirements, please refer to the MS curriculum overview. Certificate program courses are subject to the requirements of the respective certificate.

Undergraduate-level Preparatory Courses

Undergraduate credit does not count toward the MS-CFRM degree or the Computational Finance certificate requirements.

This MS-CFRM pre-program course reviews the mathematical methods fundamental for the study of quantitative and computational finance. The areas of focus include calculus and multivariable calculus, constrained and unconstrained optimization, and linear algebra.

Topics covered include the following:

Functions and inverse functions

Limits, derivatives, partial derivatives, and chain rule

Integrals and multiple integrals, changing the order of differentiation and integration

Upon completion of the course students will know the fundamental mathematical concepts needed to effectively study quantitative finance areas such as fixed income, options and derivatives, portfolio optimization, and quantitative risk management.

Upon completion of the course students will know the basic probability and statistics tools needed to effectively study quantitative finance areas such as fixed income, options and derivatives, portfolio optimization, and quantitative risk management.

This MS-CFRM pre-program course is an introduction to computational finance and financial econometrics. The course uses the material contained in CFRM 460 and CFRM 461 to build and analyze statistical models for asset returns.Topics:

Upon completion of the course, students will be able to apply the fundamental mathematical and statistical concepts needed to estimate and analyze statistical models for asset returns and to apply these models to portfolio theory and risk analysis.

Prerequisites
Prior completion or concurrent enrollment in CFRM 460 and 461 or permission of instructor.

CFRM 463: R Programming for Quantitative Finance (1)

THIS COURSE IS MANDATORY FOR CFRM MS STUDENTS UNLESS A WAIVER IS OBTAINED.

Introduction to the R programming language (r-project.org) for students who have had little to no prior computer programming experience in R. Students will learn the fundamentals of R programming.

Topics include:

R language syntax and control

R data structures

Data import and export capabilities

Functions and scripts

Graphics and plotting

R Package system

Upon completion of the course students will know to write R scripts to access data, perform basic analysis, and graphing for data visualization.

Prerequisites
Prior experience in another programming language is desirable.

Mandatory CFRM MS Courses

The following courses are part of the core Master’s program curriculum and must be completed by all students seeking the degree.

This course will introduce students entering into the CFRM MS degree and certificate programs to the fundamentals of financial derivatives. Topics will include the basics of interest rates and present value calculations, term structure of interest rates, the concepts of financial arbitrage, the pricing of futures, forwards, and call/put options, and the binomial lattice.Learning Objectives:

Upon successful completion of the course the student will be able to:

Convert between different interest rate compounding conventions

Price the current value of future cash flows and compute discount factors

Understand the term structure of interest rates

Understand the pricing of forwards and futures and the use of futures for hedging risk

Understand how to use no-arbitrage arguments to value options and financial derivatives contracts

Write computer programs to compute implied volatility and price an option contract using a binomial lattice

Prerequisites
Mathematics, probability/statistics, and R programming at the level of CFRM 460, 461, 463. Familiarity with material from ECON 424/CFRM 462 is recommended (a version of CFRM 462 is available for free on Coursera; Professor Zivot’s syllabus is also posted at http://faculty.washington.edu/ezivot/econ424/424syllabus.htm)

CFRM 541: Investment Science II (4)

This course is an introduction to the mathematical, statistical and financial foundations of investment science. Learning of the theoretical concepts will be re-enforced through use of R computing exercises. The material is similar in scope to an MBA level investments course, but at a significantly higher quantitative level.Topics include:

Prerequisites
CFRM 540 or permission of instructor. Coursework in multivariate calculus, linear algebra, and one-dimensional optimization at the level of CFRM 460, and probability and statistics at the level of CFRM 461. Familiarity with the material in CFRM 462 is desirable.

CFRM 542: Financial Data Modeling and Analysis in R(4)

This course is an in-depth hands-on introduction to the R statistical programming language (www.r-project.org) for computational finance. The course will focus on R code and code writing, R packages, and R software development for statistical analysis of financial data including topics on factor models, time series analysis, and portfolio analytics. Topics include:

This course provides basic knowledge of the theory, statistical modeling and computational methods of pricing options and other derivative products. The course blends mathematical and statistical theory with hands-on computing. The first part of the course will emphasize options on stocks, stock indices, currencies and futures, and the latter part will focus on interest rate derivatives. Course work includes assignments in theory and computation, and either a final exam or a project.

Brief review of forwards, futures, and options basics

Black-Scholes theory and dynamic hedging with the Greeks

Volatility estimation, implied volatility, the volatility smile

Option prices using additive and multiplicative binomial, and use of trinomial trees

This is a course in quantitative risk management and financial econometrics. The focus will be on the statistical modeling of financial time series (asset prices and returns) with an emphasis on modeling volatility and correlation for quantitative risk management. The learning goals/objectives of the course are to (1) survey the relevant theoretical and practical literature; (2) introduce state-of-the-art techniques for modeling financial time series and managing financial risk; (3) use the open source R statistical software to get hands-on experience with real world data. Topics to be covered include:

Empirical properties and stylized facts of asset returns

Probability distributions and statistical models for asset returns

Risk concepts

Volatility modeling

Extreme value theory

Multivariate dependence using copulas

Introduction to credit risk models and management

Prerequisites
CFRM 542 or equivalent.

CFRM 558: Fixed Income Analytics and Portfolio Management (4)

This required course in the MS CFRM program provides a solid foundation in fixed income analytics and portfolio management. Course will include some lectures on real-world fixed income applications by finance industry professional guest lecturers. Computing exercises with R will be used throughout to re-enforce understanding of the theory and methods. Topics covered will include:

Fixed income instrument types including MBS’s and municipal bonds

Fixed income data sources, access and manipulation

Term structure of interest rates and yield curve construction

Interest rate risk management

Interest rate forwards, swaps, futures and options

Introduction to binomial tree pricing of interest rate derivatives

Case studies

Upon successful completion, students will have a firm understanding of fixed income markets, data and analytics, and be able to apply this knowledge to fixed income portfolio construction, performance analysis and risk management.Prerequisites
Good understanding of multivariable calculus, linear algebra, probability, and statistics at least at the level of CFRM 460 and CFRM 461.

Elective CFRM MS Courses

The following courses may be taken as desired in order to meet the 42-credit minimum of the CFRM MS program, subject to listed prerequisites.

CFRM 500: Special Studies in Computational Finance (variable credit)

This course serves as a rotating topic determined by the faculty member or affiliate instructor teaching the course.Prerequisites
As described in course information.

CFRM 510: Financial Data Access & Analysis with SQL, VBA, Excel (4)

Working financial analytics practitioners regularly need to access data stored in SQL databases. In addition, it is common for the results of an analysis to be summarized and distributed via an Excel spreadsheet. This course provides practical lessons in the retrieval and manipulation of data using SQL, VBA, and Excel. In addition it shows how to leverage the powerful financial data modeling and analysis capabilities of R in conjunction with use of SQL, VBA and Excel. Course topics include:

SQL query development

SQL database access from R via DBI and RODBC

Data analysis with PowerPivot

Excel VBA object model and VBA procedure development

SQL database access from VBA

Web data access from VBA

Excel and R interoperability

VBA client and R server computing

Course will include a project that involves an end-to-end implementation of an analytic solution that emulates the type of implementation using SQL, VBA and Excel that will be required by a finance industry organization.

Prerequisites
CFRM 463 or instructor permission.

CFRM 520: Energy Markets Analytics and Derivatives (2)

This course looks at the practices of valuation and risk management applied to energy portfolios. A typical energy company owns and operates physical assets such as power plants, gas storage facilities and refineries, and trades in highly complex contracts. In addition, the market dynamics of commodities, especially power and gas, display characteristics such as seasonality, mean reversion and jumps, which make them non-trivial to model when compared with the equivalent financial market dynamics. In this course we look at the differences between typical financial market and energy market participants’ portfolios and give an in-depth analysis of the theory and practice for valuation and risk applied to the latter. The course will cover valuation and risk methodologies that will be applied to power, gas, and oil portfolios. The different market and credit risk metrics that are the most relevant to energy market portfolios will be discussed.
Upon completion of the course, students will be able to:

This course is an introduction to the mathematical, statistical and financial foundations of models for analyzing, predicting, and mitigating credit risks. Students will learn the theoretical basis for widely-used modeling methods for credit risk assessment and implement those methods through programming assignments using R. The course will focus on both obligor-level and portfolio-level credit risks, as well as valuation and risk analysis of assets and derivatives with credit risk. Topics include:

Prerequisites
CFRM 541 and 546 or equivalents, or permission of instrutor.

CFRM 548: Monte Carlo Methods in Finance (4)

This course covers a broad range of standard and specialized Monte Carlo methods in finance with a focus on accurate derivative pricing. Students will learn the theoretical rationale for the methods and will gain applications knowledge through programming assignments using R or Matlab. The course will begin with an overview Monte Carlo methods and a review of basic derivative pricing method. Topics covered will include:

This course introduces students to quantitative trading systems development.
Includes an overview of financial markets, instruments, exchanges, and the electronic trading process. Students will then us a paper trading account with Interactive Brokers (http://www.interactivebrokers.com) to explore electronic trading of stocks, futures, and ETFs. After this preliminary material, students will learn to use the R language for statistical computing (http://www.r-project.org) to develop, evaluate, backtest, and optimize quantitative trading strategies using the R packages xts, quantmod, blotter, quantstrat, and PerformanceAnalytics.Topics include:

Uses of indexes: benchmarking, asset allocation, and the basis for investment vehicles

Benchmark construction principles and practical issues

Index calculations, weighting, rebalancing, and maintenance

Equity style indexes

GIPS: Global Investment Performance Standards

Prerequisites
CFRM 462 and CFRM 541, or equivalent.

CFRM 553: Financial Time Series Forecasting Methods(4)

This course is an introduction to the role that forecasts can play in investment decisions, especially investing that involves views on short-term opportunities that are implemented through informed rebalancing or explicit asset class tilts away from benchmark. Learning of the theoretical concepts will be re-enforced through use of computing exercises. Topics include:

The course will focus on the endowment management process and specific challenges facing institutional fund managers. These include evaluating the role of an endowment, portfolio construction, risk management, manager selection, and alternative asset class investing. As such, the course utilizes concepts from finance and investments, macroeconomics, and mathematical optimization. Specific topics include:

Endowment policy background and philosophy

Spending

Risk and asset allocation

Emerging market investing

Fixed incomes role in endowment

Liquidity and investing in private equity.

Reading assignments will form the basis for class discussion and students are expected to be prepared for case discussions.

Prerequisites
CFRM 541 or equivalent. A general understanding of economics and a good background in core finance and portfolio optimization, e.g., CFRM 543 is preferred.

CFRM 555: Optimization Methods in Finance (4)

This course provides an introduction to numerical optimization methods in finance. The course will discuss the theory and efficient solution methods for major classes of optimization problems. Theoretical concepts will be paired with example applications and computing exercises. Homework problems will include use of an industrial strength optimizer to solve finance applications. Topics include:

This course is a practical introduction to C++ programming for financial applications. The course will focus on developing basic object oriented programming skills in C++ to implement computational finance solutions. Course coverage will also include integrating C++ applications with R and Excel. Course topics include:

C++ language, syntax, and control

Object-oriented programming

The C++ Standard library

Rcpp interface from R to C++

Rinside interface from C++ to R

xlw interface from Excel to C++

COM interface with C++ .

Prerequisites
CFRM 542 and CFRM 544, which may be taken concurrently, or permission of instructor.

CFRM 559: Stochastic Calculus for Derivatives (4)

This course provides a systematic examination of financial derivatives pricing using stochastic calculus. Examines popular stochastic differential equation models such as Geometric Brownian motion, Vasicek, Hull-White, Cox-Ingersoll-Ross, Black-Karasinski, Heath-Jarrow-Morton, and Brace-Gatarek-Musiela, as well as Poisson and Levy processes. Applications include equity, fixed-income, and credit derivatives.

This course provides an introduction to the development and assessment of statistical models used in actuarial science. For each model, equal time will be given to theory, estimation, and the application to insurance and/or risk management problems. Subject areas to be covered include:

Survival, severity, frequency and aggregate models

Estimation theory/methods and goodness of fitness tests

Credibility methods including Bayes and empirical Bayes

Simulation

Upon successful completion of this course, students will be able to identify steps in actuarial modeling, comprehend the implicit assumptions in the chosen model(s), identify which assumptions are applicable in a given business context, and make necessary adjustments to the models, as required.

This course will provide a detailed research process and tools for replicating, assessing, conceptualizing, and developing systematic trading strategies. Students will apply their knowledge in hands-on projects to replicate and evaluate existing research and to create and evaluate a new strategy model.

Development of systematic trading strategies should follow a highly scientific and repeatable process. This course will start by reviewing categories of systematic strategies, drawing out patterns followed throughout the industry.

We will demonstrate a repeatable process for evaluating ideas, constructing hypotheses, building each of the strategy components, and evaluating and improving the strategy at each step. Students will use the R Language for Statistical Computing and Graphics to replicate academic research and evaluate the claims made in papers. Students will also construct a non-trivial strategy from scratch, evaluate the power of each of its components, and examine the likelihood of overfitting. The strategy will be documented and presented in lieu of a final exam.

Special CFRM Electives

CFRM 600: Independent Research or Study (variable credit)

REQUIRES FACULTY SUPERVISION. Research project, independent academic studies outside of a regular classroom environment. Offered to qualified students with a particular interest area not fully served by existing UW graduate courses.

CFRM 601: Internship or CPT (variable credit)

REDUCED FEE RATE of $200/credit. Academic credit for industry internships and job training organized with the authorization of department staff.